All students following the course in the A.Y. 2025/2026 are requested to register in Google Classroom using the e-mail "@studenti.uniroma1.it". The registration code for the course is 4k6wcsxw.
WARNING! The course is open to other students of the Faculty and of the University who are interested in the covered items, as there are NO preparatory or mandatory courses to be taken before.
Lectures will start on Thursday the 25th of September and they will be held IN PERSON with the following general time schedule:
Thursday, hr. 15:00-17:30 (approx.), room 5, via Eudossiana 18, building A-RM031
Friday, hr. 11:00-12:30 (approx.), room 9, via Eudossiana 18, building A-RM031
N.B. There are shown actual times of lectures, bearing in mind that 15 minutes per hour are reserved for questions and discussions. Office hours are scheduled by appointment and can be held either in person or remotely.
Official site of the Master Degree in Industrial/Management Engineering
Programme A.Y. 2025/2026. The final program of the course is referring to ALL and ONLY what was presented, explained and discussed during lectures; it will be defined precisely on course also based on the students' feedback, as this is the last year and after the course will become "Advanced Neural Networks for Industrial Engineering":
Fundamentals of Machine Learning Methods:
Introduction to machine learning and data-driven modeling: data preparation; generalization; regularization and structural optimization. Overview of the main clustering and classification methods.
Neural networks and Deep Learning:
Overview of shallow feed-forward and recurrent neural networks. Introduction to Deep Learning, specific problems and solutions (double descent, vanishing/exploding gradient, barren plateau, dropout, ensembling, weight initialization). Deep feed-forward and recurrent neural networks. Generative and diffusive systems (GAN, VAE, etc.).
Introduction to Hyperdimensional Computing:
Fundamentals of hyperdimensional computing: data representation through high-dimensional vectors and their sparse distribution properties and robustness. Vector symbolic architectures and computational models for learning and memory. Binding and superposition methods for encoding complex information. Associative memory management and hybrid Quantum-HDC approaches. HDC-based learning strategies for classification, regression, pattern recognition and eXplainable AI problems. Practical applications of HDC in the domains of industrial and information engineering, time series analysis, anomaly detection, and resource-constrained embedded systems.
Introduction to Quantum Computing:
Introduction to quantum computing. Elementary unitary transformations, Quantum Gate Arrays. Main quantum optimization algorithms, adiabatic approaches, Quantum Approximate Optimization Algorithm (QAOA). Variational approaches, Quantum Machine Learning and Quantum Neural Networks.
Hands-on practices using Python and Matlab:
classification and clustering;
linear regression, overfitting and underfitting;
deep learning;
quantum programming and simulation;
quantum deep learning;
energy time series prediction;
graph neural networks;
behavioral analysis.
Applications and case studies:
prediction of renewable energy sources, intelligent energy systems, smart grids;
applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.);
behavioral analysis and biometrics;
analysis of materials and industrial processes;
machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks;
federated and distributed learning systems;
quantum neural networks, quantum optimization, and quantum generative models.
Exams Timetable A.Y. 2025/2026. Exams may be taken by appointment when it is deemed most appropriate starting from January 2026; the exam registration will take place in the official time windows provided by the Faculty calendar, as shown below:
1st round: January 2026
2nd round: February 2026
Extra round: March/April 2026
NOTE. Reserved to the categories of students indicated in the art. 40, par. 6 of the General Manifest of Studies ("Manifesto Generale degli Studi") of the University "La Sapienza". NO EXCEPTIONS ARE ALLOWED.
3rd round: June 2026
4th round: July 2026
5th round: September 2026
Extra round: October/November 2026
NOTE. Reserved to the categories of students indicated in the art. 40, par. 6 of the General Manifest of Studies ("Manifesto Generale degli Studi") of the University "La Sapienza" , as well as to failing students and to students enrolled for A.Y. 2025/2026 in the 2nd year of the Master Degree. NO EXCEPTIONS ARE ALLOWED.
Teaching Material:
M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer Nature, Switzerland, 2018
Notes, slides and handouts provided by the Teachers:
[01-Intro_ML] [02-HandsOn_NN] [03-Clustering] [04-CNNs] [05-DRNNs]
Note: The Python and Matlab scripts used during the hands-on exercitations have been shared on Google Classroom with registered students who attended the course.
Further reading (optional):
C.C. Aggarwal, Neural Networks and Deep Learning, Springer Cham, Switzerland, 2023
S. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson, NJ, USA, 2009
O. Simeone, An Introduction to Quantum Machine Learning for Engineers, arXiv preprint [2205.09510], 2022
NOTICE. For each type of communication or inquiries related to the course, students are kindly requested to send me an e-mail writing in the SUBJECT "Machine Learning IE" and in the text body the following data: name, surname and university ID number. I will try to answer as soon as possible.